Background
Protected areas are the most common and important instrument for the conservation of biological diversity and are called for under the United Nations' Convention on Biological Diversity. Growing human population densities, intensified land-use, invasive species and increasing habitat fragmentation threaten ecosystems worldwide and protected areas are often the only refuge for endangered species. Climate change is posing an additional threat that may also impact ecosystems currently under protection. Therefore, it is of crucial importance to include the potential impact of climate change when designing future nature conservation strategies and implementing protected area management. This approach would go beyond reactive crisis management and, by necessity, would include anticipatory risk assessments. One avenue for doing so is being provided by simulation models that take advantage of the increase in computing capacity and performance that has occurred over the last two decades.
Here we review the literature to determine the state-of-the-art in modeling terrestrial protected areas under climate change, with the aim of evaluating and detecting trends and gaps in the current approaches being employed, as well as to provide a useful overview and guidelines for future research.
Results
Most studies apply statistical, bioclimatic envelope models and focus primarily on plant species as compared to other taxa. Very few studies utilize a mechanistic, process-based approach and none examine biotic interactions like predation and competition. Important factors like land-use, habitat fragmentation, invasion and dispersal are rarely incorporated, restricting the informative value of the resulting predictions considerably.
Conclusion
The general impression that emerges is that biodiversity conservation in protected areas could benefit from the application of modern modeling approaches to a greater extent than is currently reflected in the scientific literature. It is particularly true that existing models have been underutilized in testing different management options under climate change. Based on these findings we suggest a strategic framework for more effectively incorporating the impact of climate change in models exploring the effectiveness of protected areas.
While several empirical and theoretical studies have clearly shown the negative effects of climate or landscape changes on population and species survival only few of them addressed combined and correlated consequences of these key environmental drivers. This also includes positive landscape changes such as active habitat management and restoration to buffer the negative effects of deteriorating climatic conditions. In this study, we apply a conceptual spatial modelling approach based on functional types to explore the effects of both positive and negative correlations between changes in habitat and climate conditions on the survival of spatially structured populations. We test the effect of different climate and landscape change scenarios on four different functional types that represent a broad spectrum of species characterised by their landscape level carrying capacity, the local population turnover rates at the patch level (K-strategies vs. r-strategies) and dispersal characterstics. As expected, simulation results show that correlated landscape and climatic changes can accelerate (in case of habitat loss or degradation) or slow down (in case of habitat gain or improvement) regional species extinction. However, the strength of the combined changes depends on local turnover at the patch level, the overall landscape capacity of the species, and its specific dispersal characteristics. Under all scenarios of correlated changes in habitat and climate conditions we found the highest sensitivity for functional types representing species with a low landscape capacity but a high population growth rate and a strong density regulation causing a high turnover at the local patch level.
The relative importance of habitat loss or habitat degradation, in combination with climate deterioration, differed among the functional types. However, an increase in regional capacity revealed a similar response pattern: For all types, habitat improvement led to higher survival times than habitat gain, i.e. the establishment of new habitat patches. This suggests that improving local habitat quality at a regional scale is a more promising conservation strategy under climate change than implementing new habitat patches. This conceptual modelling study provides a general framework to better understand and support the management of populations prone to complex environmental changes.
In common garden experiments, a number of genotypes are raised in a common environment in order to quantify the genetic component of phenotypic variation. Common gardens are thus ideally suited for disentangling how genetic and environmental factors contribute to the success of invasive species in their new non-native range. Although common garden experiments are increasingly employed in the study of invasive species, there has been little discussion about how these experiments should be designed for greatest utility. We argue that this has delayed progress in developing a general theory of invasion biology. We suggest a minimum optimal design (MOD) for common garden studies that target the ecological and evolutionary processes leading to phenotypic differentiation between native and invasive ranges. This involves four elements: (A) multiple, strategically sited garden locations, involving at the very least four gardens (2 in the native range and 2 in the invaded range); (B) careful consideration of the genetic design of the experiment; (C) standardization of experimental protocols across all gardens; and (D) care to ensure the biosafety of the experiment. Our understanding of the evolutionary ecology of biological invasions will be greatly enhanced by common garden studies, if and only if they are designed in a more systematic fashion, incorporating at the very least the MOD suggested here.
Plant population modelling has been around since the 1970s, providing a valuable approach to understanding plant ecology from a mechanistic standpoint. It is surprising then that this area of research has not grown in prominence with respect to other approaches employed in modelling plant systems. In this review, we provide an analysis of the development and role of modelling in the field of plant population biology through an exploration of where it has been, where it is now and, in our opinion, where it should be headed. We focus, in particular, on the role plant population modelling could play in ecological forecasting, an urgent need given current rates of regional and global environmental change. We suggest that a critical element limiting the current application of plant population modelling in environmental research is the trade-off between the necessary resolution and detail required to accurately characterize ecological dynamics pitted against the goal of generality, particularly at broad spatial scales. In addition to suggestions how to overcome the current shortcoming of data on the process-level we discuss two emerging strategies that may offer a way to overcome the described limitation: (1) application of a modern approach to spatial scaling from local processes to broader levels of interaction and (2) plant functional-type modelling. Finally we outline what we believe to be needed in developing these approaches towards a 'science of forecasting'.